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ICASSP
2011
IEEE
12 years 11 months ago
Langevin and hessian with fisher approximation stochastic sampling for parameter estimation of structured covariance
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
Cornelia Vacar, Jean-François Giovannelli, ...
WSC
2004
13 years 8 months ago
On Using Monte Carlo Methods for Scheduling
Monte Carlo techniques have long been used (since Buffon's experiment to approximate the value of by tossing a needle onto striped paper) to analyze phenomena which, due to ...
Samarn Chantaravarapan, Ali K. Gunal, Edward J. Wi...
CDC
2009
IEEE
143views Control Systems» more  CDC 2009»
13 years 10 months ago
Parameter approximate dynamic optimization for PSO systems
— This paper presents a novel swarm approximate dynamic programming method (swarm-ADP) for parameter optimization of PSO systems, from the perspective of optimal control. Based o...
Qi Kang, Lei Wang, Derong Liu, Qidi Wu
ESA
2006
Springer
136views Algorithms» more  ESA 2006»
13 years 11 months ago
Approximation in Preemptive Stochastic Online Scheduling
Abstract. We present a first constant performance guarantee for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release ...
Nicole Megow, Tjark Vredeveld
ATAL
2007
Springer
14 years 1 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone